직원·HR

Measures the gap between the values a company says it holds and the values employees actually experience in day-to-day decisions, priorities, and leadership behavior. Built for HR and leadership teams auditing culture health, with an AI follow-up that reconstructs a real recent moment where a decision either honored or contradicted a stated value.

압축 근무제 또는 근무 시간 단축 근무제 도입과 관련한 직원의 관심도, 일정 선호도, 수용 가능한 절충안, 운영상의 우려 사항을 측정합니다. 근무 주 정책 변경을 검토하는 인사팀 및 리더십 팀을 위해 설계되었습니다.

신규 고객이 계약 체결 후 실질적인 가치를 얻기까지 얼마나 순조롭게 진행되었는지를 측정합니다 — 커뮤니케이션, 다음 단계의 명확성, 가치 실현 시간을 다루며, 단일 만족도 점수에 의존하는 대신 마찰이 정확히 어디에서 발생했는지를 재구성하는 AI 후속 질문을 포함합니다.

Measures employee Net Promoter Score on a standard 0–10 scale and identifies the workplace factors driving or hindering advocacy, enabling targeted improvements to employer brand, engagement, and retention.

인재 개발 전략 및 유지 이니셔티브에 정보를 제공하기 위해 직원들의 성장 기회 접근성, 멘토링 품질, 경력 경로의 명확성, 내부 이동 자신감을 측정합니다.

UX 및 정책 개선을 위해 최근 4주 동안의 좌석/핫데스킹 시스템 사용성, 배정 공정성, 접근 장벽에 대한 직원들의 인식을 측정합니다.

Measures employee comfort with workplace monitoring practices, evaluates policy transparency, and identifies trust gaps to guide privacy, consent, and communication improvements.

A structured self-assessment covering goal achievement, manager support, and growth priorities for the current review period, for use in performance reviews or pulse check-ins. An AI follow-up interview reconstructs the specific achievement or obstacle behind the respondent's self-rating instead of leaving it as an abstract number.

A monthly pulse survey measuring hospitality employees' perceptions of role clarity, coaching quality, recognition, and workplace support over the past 30 days. Designed for property-level or multi-unit tracking with built-in segmentation by role, tenure, and shift.

Measures how included, fairly treated, and heard employees feel across backgrounds, and prioritizes where DEI investment would have the most impact. An AI follow-up interview digs into specific belonging or fairness experiences behind the scores instead of leaving them as abstract ratings.

콜센터 상담원의 통화 품질, 규정 준수, 소프트 스킬을 관리자 관점에서 평가하며, 베스트-워스트 방식의 교육 우선순위 순위 매기기와 평가 근거를 설명하기 위해 최근 특정 통화를 재구성하는 AI 후속 인터뷰가 함께 제공됩니다. 주기적 성과 리뷰를 진행하는 팀장 및 품질 분석가를 위해 설계되었습니다.

Measures employee participation patterns, motivational drivers, and barriers to workplace wellness engagement. Designed for HR and wellness teams seeking actionable data to improve program design, communication, and uptake.

Measures how employees actually feel about the branded merchandise they receive — quality, frequency of use, and which items feel worth keeping — plus a best-worst trade-off to prioritize future swag spend. An AI follow-up interview digs into the story behind their most memorable item to surface what actually makes swag feel meaningful versus wasted.

A voice AI exit interview departing employees can take privately, on their own time. The interviewer covers reasons for leaving, manager and culture experience, and what would have changed their mind — probing gently where a form gets one-word answers. Structured questions capture the benchmarkable basics.

Measures how employees actually experience the meetings on their calendar — frequency, agenda discipline, time well spent, and what they'd fix first — with an AI follow-up that reconstructs a specific meeting that felt like a waste of time and what would have made it worth attending.

Gathers structured multi-rater feedback—from managers, peers, direct reports, and self—on an employee's core competencies and overall effectiveness. An AI follow-up interview digs into the concrete behavioral examples behind the ratings, turning vague impressions into specific, actionable evidence for development conversations.

Measures how attuned staff feel to patients' needs and how safe they feel speaking up about care or safety concerns, surfacing barriers to raising issues and signs of moral distress. Built for healthcare and care-setting HR and quality teams, with an AI follow-up that reconstructs what actually happened the last time an employee flagged a concern.

Identifies where new users encounter friction during signup, setup, and first use, enabling product teams to diagnose activation blockers and optimize time-to-value.

A CNBC|SurveyMonkey-style workforce pulse that tracks how happy, secure, and engaged employees feel right now — covering pay fairness, workload, leadership trust, and flight risk. An AI follow-up interview digs into the story behind low happiness or job-security scores so HR and leadership see the real drivers, not just the number.

Evaluates the clarity, timing, and helpfulness of in-product onboarding guidance (tooltips, walkthroughs, checklists) to identify friction points and improve activation for new users.